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Comparison between clinicians’ reviews of verbal autopsies and a probabilistic model in Zimbabwe

Comparison between clinicians’ reviews of verbal autopsies and a probabilistic model in Zimbabwe. Ron Mataya, Velda Mushangwe, Edward Fottrell, Stephen Munjanja. Background. Zimbabwe maternal and perinatal mortality study 2007-8 Causes of maternal deaths identified by

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Comparison between clinicians’ reviews of verbal autopsies and a probabilistic model in Zimbabwe

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  1. Comparison between clinicians’ reviews of verbal autopsies and a probabilistic model in Zimbabwe Ron Mataya, Velda Mushangwe, Edward Fottrell, Stephen Munjanja

  2. Background • Zimbabwe maternal and perinatal mortality study 2007-8 • Causes of maternal deaths identified by • Review of medical records • Verbal autopsy

  3. Definitionof verbal autopsy: • Verbal autopsy: interviewing of family members about the circumstances of a death at home to arrive at a cause of death (WHO)

  4. Rationale for the study • Many deliveries and deaths occur at home • Facility-based causes of deaths are an inaccurate reflection of cause-specific mortality • Interventions to prevent maternal deaths should be based on population-based data

  5. Study objective: • To determine causes of death among women of reproductive age dying at home • To compare the causes of death as determined by 3 physicians • To assess the performance of a probabilistic model (InterVA-M, (www.interva.net) against the physicians

  6. Study Design • Population based study of pregnancy outcomes in 11 randomly selected districts in Zimbabwe • All maternal deaths were recruited during the study period

  7. Determination of Pregnancy Status at Death • Pregnant at death • Not pregnant within 6 weeks of death • Pregnancy ended within 6 weeks of death

  8. Physician coding for pregnancy status • In 82/87 of cases (94%) 2 physicians independently reached the same conclusion • Disagreed in 5 cases (6%) • Overall kappa statistic for agreement in relation to pregnancy status was 0.89;

  9. Results: Physician coding for causes • Reached the same cause of death in 38% (n=33) of cases • In 44 cases (51%) 2 out of the 3 reached the same cause of death • In 10 (11%) each reached a different conclusion • Combined kappa for the 3 reviewers is 0.49, representing a fair agreement

  10. Results: Pregnancy status of the 87 deaths as classified by InterVA-M & 2 physicians

  11. Results: InterVA-M; causes • Each death assigned a probability • Probability used in weighting the causes in calculating cause-specific mortality fractions (CSMF) for the whole sample • InterVA-M quoted 2 causes with the highest probability in 66 cases (72%) • Single cause in 15 cases (17%) • 3 causes in 6 cases (7%) • Overall certainty for causes assigned to each case by InterVA-M was 53%

  12. Comparison of major cause categories

  13. Agreement between physicians and InterVA-M • In 80 cases (92%) the InterVA-M determined pregnancy status agreed with at least one of the two coding physicians • In 69% of cases at least one of the InterVA-M causes agreed with at least one of the physician diagnoses • In 27 cases (31%) no agreement between InterVA-M and physician diagnoses

  14. Conclusion • Population-level cause-specific mortality fractions generally compared well between the InterVA-M approach and physicians reviews • The InterVA-M method continues to show promise as an efficient and reliable tool for ascertaining major population-level mortality burdens required for health planning, monitoring and program assessment.

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